Columbia–NCAER Conference on Trade, Poverty, Inequality and Democracy

New Delhi March 31–April 1, 2011

Paper 7

India: Election Outcomes and Economic Performance*

Poonam Gupta Indian Council on Research in International Economic Relations

Arvind Panagariya Columbia University

* This paper is a product of the Columbia Program on Indian Economic Policies at the School of International and Public Affairs, Columbia University. The program is supported by a generous grant from the John Templeton Foundation. The opinions expressed in the paper are those of the authors and do not necessarily reflect the views of the John Templeton Foundation.

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India: Election Outcomes and Economic Performance

Poonam Gupta

Arvind Panagariya*

This draft: March 8 2011

Abstract

In this paper we provide the first analysis of the relationship of growth to election outcomes in India. Using a comprehensive data set consisting of all candidates contesting the election, we also provide the first systematic quantitative analysis of the 2009 Lok Sabha elections. Our key result is that superior growth performance at the level of the state gives a definite advantage to the candidates of the state incumbent party in the constituencies of that state. We offer two additional results: personal characteristics such as education and wealth have at most a small impact on election outcomes; and, at least in the 2009 election, incumbency at all levels—candidate as well at the party level in the center and state government, contributed positively to election prospects of a candidate.

* The authors are at Indian Council on Research in International Economic Relations, New Delhi and Columbia University, New York. They can be reached at pgupta@icrier.res.in and ap2231@columbia.edu, respectively. We would like to thank Laveesh Bhandari for sharing the data for social and economic indicators of the constituencies with us, the participants of the Annual Growth and Development Conference at ISI, Delhi for many useful comments, and to Ganesh Manjhi and Anjum Khalidi for excellent research assistance. Work on this paper has been supported by Columbia University’s Program on Indian Economic Policies, funded by a generous grant from the John Templeton Foundation. The opinions expressed in the paper are those of the authors and do not necessarily reflect the views of the John Templeton Foundation.

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Table of Contents

1. INTRODUCTION................................................................................................................. 1

2. THE KEY RESULT: A QUICK PREVIEW ..................................................................... 5

3. THE RELEVANT LITERATURE ..................................................................................... 7

4. SALIENT FEATURES OF THE 2009 ELECTION ....................................................... 12

5. CHARACTERISTICS OF THE CONSTITUENCIES................................................... 15

6. CHARACTERISTICS OF THE CANDIDATES ............................................................ 18

6.1. WEALTH ........................................................................................................................ 18

6.2. EDUCATION.................................................................................................................... 19

6.3. CRIMINAL CASES ........................................................................................................... 21

6.4. DISTRIBUTION OF CANDIDATES BY GENDER.................................................................. 23

6.5. INCUMBENTS AND NON-INCUMBENTS COMPARED......................................................... 23

7. REGRESSION MODEL AND RESULTS ....................................................................... 25

7.1. THE EMPIRICAL MODEL................................................................................................. 25

7.2. SELECTION OF STATES ................................................................................................... 29

7.3. DEFINING INCUMBENCY................................................................................................. 30

7.4. DEFINING THE ECONOMIC PERFORMANCE ..................................................................... 32

7.5. INTERACTION BETWEEN PERFORMANCE AND INCUMBENCY .......................................... 35

7.6. CANDIDATE SPECIFIC VARIABLES.................................................................................. 35

7.7. STATE SPECIFIC AND PARTY SPECIFIC VARIABLES AND FIXED EFFECTS ....................... 35

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7.8. SIZE OF THE PARTY ........................................................................................................ 36

7.9. PRINCIPAL REGRESSION RESULTS.................................................................................. 36

7.10. FURTHER ROBUSTNESS CHECKS ................................................................................ 45

8. CONCLUDING REMARKS ............................................................................................. 49

1. Introduction

Indian election results often spring surprises. It was particularly the case when the

Bhartiya Janata Party (BJP), which led the National Democratic Alliance (NDA)

government, unexpectedly lost the 2004 Lok Sabha election. Many critics of economic

reforms celebrated the outcome as a vote against the reforms.1 Since the NDA ally and

Andhra Pradesh Chief Minister Chandrababu Naidu, who had been strongly identified

with economic reforms, also suffered a bitter defeat in the state elections held

simultaneously, this view gained additional currency. An alternative explanation offered

for this outcome was the anti-incumbency factor.2 This view assumed without offering

the underlying reason that the Indian voters preferred change to status quo, and hence

voted against the incumbent.

But the outcome of the 2009 national elections seemingly went against this latter

hypothesis: it returned the Indian National Congress, the main ruling party, to power with

significantly larger number of seats in the Lower House of the Parliament. This time

around, the state elections held in 2009 also returned the incumbent state governments in

many states such as Andhra Pradesh, Orissa, Maharashtra and Haryana. These outcomes

also seemed to contradict the incumbency disadvantage hypothesis.

Election outcomes in India, thus, seem not to show a clear pattern in terms of

incumbency disadvantage. At the same time, neither the government of the Congress led

United Progressive Alliance (UPA) nor other governments have disavowed the reforms

1 “Lok Sabha,” translated as the “House of People,” is the lower house of the Indian Parliament. For purposes of elections to Lok Sabha, the country is divided into 543 constituencies, principally on the basis of population, with each constituency electing one member. Elections to the upper house, called Rajya Sabha, are indirect with the vast majority of its members elected by the state legislative assemblies. 2 The political-economy literature refers to this view as the incumbency disadvantage.

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let alone reverse them. Seen this way, the election outcomes in India remain something of

a puzzle.

In this paper, we take the first stab at a systematic quantitative analysis of the

determinants of election outcomes in India using the data for 2009 national elections. Our

analysis focuses on the personal characteristics of the candidates such as their wealth and

education levels and the role incumbency may play at the level of the candidate as well as

parties in power at national and state levels. Most importantly, we ask whether growth at

the state level has a perceptible impact on victory prospects of the candidates contesting

on the ticket of the party in power in the state. We ask whether the candidates of the main

ruling party at the center and state enjoy an advantage in states experiencing superior

growth outcomes and suffer a disadvantage in states with poor growth outcomes.

Given the relative ease of gathering the candidate-specific data for more recent

elections, our analysis focuses on the latest 2009 parliamentary election. The 2009

election is of interest in its own right as well since, like the 2004 election, it too carried a

large element of surprise. Given the general disarray in both the Congress-led UPA,

which ruled during 2004-09, and the BJP, the main opposition party, predictions of the

election results varied widely from marginal victories for the UPA and NDA to the

emergence of a “Third Front” consisting of a group of the left-of-center parties. Yet,

defying all forecasts, the Congress greatly increased its tally from 145 to 206 seats and

comfortably formed government with a group of smaller parties.

To carry out our analysis, we assemble a large new data set covering all 8,071

candidates that contested the 2009 election. The data set includes several relevant

characteristics of all candidates, their party affiliation, their incumbency status as

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candidates, the incumbency status of their parties at the center and in the state in which

their constituencies are located, and the relative growth rates of various states. The

candidate specific information includes gender, education, wealth and criminal record of

the candidates and is compiled from the affidavits that the Election Commission requires

each candidate to file with his or her nomination.3

Our main results may be summarized as follows. First, the 2009 Parliamentary

election shows very strongly that controlling for other relevant determinants of election

outcomes, the advantage enjoyed by candidates of the incumbent party over those of non-

incumbent parties is greater in a state that grows faster than the national average than that

enjoyed in a state that grows at the national average. Symmetrically, the advantage

enjoyed by candidates of the incumbent party over those of non-incumbent parties is

smaller in a state that grows slower than the national average than that enjoyed in a state

that grows at the national average. The larger the deviation from the national growth rate,

the larger is this effect in either direction. Second, on average, incumbency at all levels

was helpful in winning the 2009 election. That is to say, on average, incumbent

candidates enjoyed an advantage over non-incumbent candidates, candidates of

incumbent national parties over those of non-incumbent national parties and candidates

of a state incumbent party within the constituencies of that state over those of non-

incumbent state parties. This incumbency effect could be due to a variety of reasons such

as the incumbent candidates and parties having more resources to spend on election

3 The Election Commission requires each candidate contesting an election for the Lok Sabha, Rajya Sabha, or state assemblies to file these affidavits since 2002. The first general election when the affidavits were submitted is 2004.

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campaigns, better name recognition or even being more charismatic.4 Our results here do

not separate the pure incumbency effect on which a great deal of the political science

literature focuses from other effects that may be associated with the attributes of and

resources available to incumbent candidates. Finally, we also find that on average, more

educated and wealthier candidates have a better chance of victory. These advantages turn

out to be far more important in the states exhibiting low growth and indeed become

statistically insignificant in states exhibiting high growth rates.

The idea pursued here is similar to the one proposed in an op-ed article in Wall

Street Journal by Bhagwati and Panagariya (2004). Commenting on the trend that shows

that anti incumbency seems to have become more dominant in Indian elections since

1991, they propose that in more recent years voters have started taking into account the

economic performance to decide whether to vote in favor of or against the incumbents.

Whereas in earlier years during the 1950s through the mid 1980s when the overall

economic performance in general was not impressive, people saw no perceptible change

in their lives, which led them to turn extremely pessimistic in so far as their economic

fortunes were concerned. Resigned that a significant change was impossible, their voting

decision was perhaps based on other factors, which often resulted in the incumbent

Congress Party being voted back to power. With the high growth of the 1980s and

thereafter, when incomes began to grow at higher rates on a sustained basis and poverty

began to decline, people’s aspirations were fundamentally altered: having experienced

change for the better, they wanted more of it and sooner than later. And if a current

government would not deliver it, they would look for another one. Thus Bhagwati and

4 In future work we plan to include the length of the incumbency to see whether an incumbency fatigue sets in after a long spell of incumbency.

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Panagariya (2004) propose that in more recent years economic performance has become

an important determinant of the way voters behave, and it perhaps explains why anti

incumbency has become a more prominent feature of election outcomes.5 We offer a

more detailed discussion of the relevant literature in Section 3.

The paper is organized as follows. In Section 2, we offer a quick preview of our

main result. In Section 3, we discuss the literature on elections in general and that on

elections in India in particular. In Sections 4 we describe some salient features of the

2009 election. In Sections 5 and 6, we summarize the relevant characteristics of the

constituencies and candidates, respectively. In Section 7, we present the empirical model

and regression results and in Section 8, we conclude the paper.

2. The Key Result: A Quick Preview

We find it useful to give a preview of our main result at the outset. This requires

us to define the incumbent party at the state level and to define the relative economic

performance of the states and divide the states into high- and low-growth states. We

define as the incumbent party the main ruling party in power (or two main parties if they

shared power) in 2007 and the preceding two or more years. This means that if a state

legislative assembly election is held in 2008 or 2009 and the government changes hands,

the outgoing party is still considered the incumbent in that state for purposes of the 2009

national elections, which were held in April and May of that year.

To group the states on the basis of growth performance, we first identify 19 major

states counting Delhi as a state and excluding the union territories, seven northeastern

5 Linden (2004) suggests that the proliferation of parties in recent years and increased competition may have further contributed to voters voting for alternative parties and against the incumbents.

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states, Sikkim, Karnataka and Jammu and Kashmir. The reasons for the exclusion of

these states are discussed in Section 7. But to ensure that the trends reported here are not

influenced by the choice of states, we later carry out various checks and find the results to

be robust. We calculate the average growth rates in these 19 states between 2004-05 and

2008-09 and rank them in declining order of the growth rates.6 This allows us to divide

the states into three groups of roughly equal number of states exhibiting high, medium

and low growth rates.

Figure 1: Proportion of the Candidates of the Incumbent Party in the State Winning the National Election (sorted by the states growth rates)

0

10

20

30

40

50

60

70

80

90

High Growth Medium growth Low Growth

Seats won as % of Seats Contested by Incumbent Party (at state level)

6 Years 2004-05 and 2007-08 and other similarly expressed periods refer to India’s financial year, which begins on April 1 and ends on March 31. Therefore, 2004-05 stands for the period from April 1 2004 to March 31, 2005.

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Armed with this classification of the states and the definition of the incumbent

party, we can ask the following key question: what proportion of the candidates fielded

by the state incumbent party in the Lok Sabha constituencies located in that state won the

national election? The outcome is depicted in Figure 1. Remarkably, incumbent parties

in the high-growth states won 85 percent of the seats they contested. In contrast, those in

medium and low growth states could win only approximately 52 and 40 percent of the

seats contested, respectively. This strong relationship between growth performance and

election outcomes handsomely survives every model modification we consider in our

regression analysis in Section 7.

3. The Relevant Literature

A large body of the literature on electoral competition developed in the context of the

western democracies employs the principal-agent framework and focuses on how the

desire to win elections conditions the behavior of politicians. This literature asks how

political incumbents might try to maximize their chances of reelection through tax and

expenditure policies favorable to their constituencies, cast legislative votes that conform

to the ideological make-up of their constituencies and exchange political favors for

campaign contributions.7 Given that our objective is to study the determinants of electoral

outcomes rather than incumbent behavior to maximize the chances of electoral victory,

this literature is at best indirectly relevant to our work.

7 For example, Rogoff and Sibert (1988) and Alesina and Rosenthal [1989] analyze the use of fiscal and monetary policy actions and Besley and Case (1995) of tax-expenditure choices by incumbents to gain electoral support. Levitt and Poterba (1994) study the effect of Congressional Representation on state economic growth. Levitt (1994), Baron (1989) and Snyder (1990) examine the response of politicians to campaign contributions. Lee (2001) provides additional references.

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A different strand of the literature examines whether incumbency by itself is an asset

or liability in elections. This literature is closer to our paper in that it focuses on the

determinants of election outcomes but it is somewhat narrowly focused on the

identification of the incumbency advantage. The literature stems from the fact that higher

unconditional probability of victory of an incumbent over non-incumbents may be the

result of selection bias and therefore need not represent incumbency advantage per se.

Conversely, a lower unconditional probability of victory of the incumbent may not

represent incumbency disadvantage. Incumbents may win more frequently simply

because they happen to be better candidates or have more resources to spend on

campaigns. Alternatively, if incumbents lose more frequently than non-incumbents, this

may be simply because they fail to keep a number of inconsistent promises made in the

prior election or because they prove themselves to be inept during their term. Therefore,

the observed frequencies of losses and wins by incumbents are by themselves insufficient

to isolate the effect of incumbency. The most compelling approach to identifying the

impact of incumbency is regression discontinuity, which tries to identify incumbents and

non-incumbents who are otherwise identical in all respects and compares their

probabilities of victory in election.8

The literature that is closest to our work is the one that looks at the impact of

economic growth on the reelection prospects of the incumbents. The earlier work on this

issue included papers by Fair (1978), Lewis-Beck (1988), Powell and Whitten (1993),

and Alesina and Rosenthal (1995). Their results showed that economic growth had no

8 An excellent example of this analysis is Lee (2001). A vast body of political science literature is devoted to the analysis of the incumbency effect in election outcomes. For example, see Erikson 1971, Collie 1981, Garand and Gross 1984, Jacobson 1987, Payne 1980, Alford and Hibbing 1981, and Gelman and King 1990 and Lee 2001.

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significant impact on the reelection prospects of the incumbents except in the US, where

the effect was positive. The more contemporary work on this is by Brender and Drazen

(2008). This paper uses cross-country data distinguishing between developed and

developing countries and between established and newer democracies. The authors find

that economic growth increases the reelection prospects of incumbents in the developing

but not developed countries.

We address this issue at a more detailed level and in the specific context of India

where incumbency can be defined at different levels.9 We exploit the variation in

economic growth across Indian states and measure the extent to which economic

performance at the state level determines the election outcome. We also do a more

detailed analysis of incumbency by distinguishing incumbency at the candidate level, at

the party level in the national government and at the party level in the state government.

In addition, we are able to assess the effect of individual characteristics on electoral

outcomes. Since the work we do here is conducted at the candidate level it is less

susceptible to endogeneity problems. A final important and interesting feature of our

analysis is that it relies on data from states that belong to a single developing country,

which has been a democracy throughout its history since the independence. Therefore,

neither the distinction between developing and developing countries nor the length of

democracy, critical to the conclusions of Breder and Drazen, plays a substantive role in

our analysis. Thus, we are able to show that even when all units of analysis (Indian states)

are developing and have the same uniform record of democracy throughout the relevant

9 Bhalla has frequently relied on economic performance as a tool of forecasting the election outcomes. For example, see Bhalla (1999, 2009).

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history, differential growth outcomes can have significant influence on the election

outcomes.

In the Indian context, the literature on the incumbency advantage or disadvantage is

relatively new. In an as yet unpublished paper, Linden (2004) uses the regression

discontinuity approach and finds that prior to 1991, incumbents had enjoyed an

advantage over non-incumbents. But beginning in 1991, this relationship reversed with

incumbents suffering a disadvantage. For the elections from 1991 to 1999, he estimates

that on average incumbents were 14 percentage points less likely to be elected than

comparable non-incumbents.10 He reaches this conclusion by comparing the probabilities

of victory of candidates in an election that had barely won (incumbents) to those of the

candidates who barely lost (non-incumbents) the prior election. The underlying

assumption is that the candidates that just win and those that just lose an election are

identical in all respect and any advantage or disadvantage to a victorious candidate

(incumbent) in the following election must result from incumbency.

While Linden (2004) studies incumbency disadvantage at the level of the candidate, a

number of descriptive analytic studies following the 2004 election have focused on the

disadvantage arising from association with an incumbent party. Panagariya (2004), and

Yadav (2004) note that on average the state ruling parties performed poorly in the 2004

national elections in the constituencies located in their own states but with one major

exception: candidates of parties that had defeated the party in power in a state election

held just prior to the national election did well in the latter as well. Yadav characterizes

the one to two-year period between the state and national elections when the state ruling

10 Uppal (2005) also finds that incumbency has hurt the candidates in recent Indian elections.

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party has just come in power as the “honeymoon” period during which the latter’s

candidates (i.e., candidates of the recently empowered incumbent party in the state) enjoy

a positive advantage.

Panagariya (2004) states, “The results [of 2004 Parliamentary elections] broadly

reflect an anti-incumbency vote principally at the state level. Even where anti-

incumbency explanation does not apply, the state-level politics rather than a rural-urban

split remains the decisive factor. Until recently, Rajasthan, Madhya Pradesh and

Chhattisgarh had Congress governments, which had pursued policies centered on rural

development, primary education and health. Nevertheless, in the state-level elections in

December 2003, the Congress governments in all three states lost by landslides to the

BJP and its allies. In the current parliamentary elections, all three states voted

overwhelmingly for the BJP and its allies. In the December 2003 state elections, the

Congress had managed to retain power in Delhi and it swept there in the parliamentary

elections as well.”

Ravishankar (2009) carries out a quantitative analysis of the prospects of victory for

the incumbent candidates of the main party in power relative to the incumbent candidates

of the main opposition party using the national and state election data from 1977 to 2005.

Because her analysis is strictly restricted to incumbent candidates, it does not compare

incumbent and non-incumbent candidates. She finds that setting aside the parties in their

honeymoon period, incumbent candidates of the main party in power in both national and

state elections face higher probability of loss in their reelection bids than the incumbent

candidates of the main opposition party. Ravishankar (2009) also finds a cross effect

flowing from party incumbency at the national level to state elections and vice versa.

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Once again, setting aside the parties in their honeymoon period, incumbent candidates of

the main party in power at the center face a higher probability of defeat than the

incumbent candidates of the main opposition party at the center. Symmetrically,

incumbent candidates of a party in power in a state face a higher probability of defeat in

the national election than the incumbent candidates of the main opposition party within

that state.

A key shortcoming of Ravishankar (2009) is that it excludes non-incumbent

candidates. If the incumbency effect is associated with the party in power, there is no

reason why it would not apply to non-incumbent candidates contesting the election on the

incumbent party’s ticket. Our data set, though confined to the 2009 national elections,

includes all candidates and therefore allows for more complete test of the incumbency

effect at the level of the party.

4. Salient Features of the 2009 Election

To provide some background, Table 1 reports the broad results of the elections held

in 1999, 2004 and 2009. It shows that the national parties numbering six or more have

won only a little more than two-thirds of the seats in each of the three elections.11 As a

result, the party winning the largest number of seats has fallen well short of the majority

so that each government has been based on a multi-party coalition. Because the party

11 India has more than one thousand registered political parties. These are divided into national parties, state parties and other (unrecognized) parties. Any registered party that lacks the status of state or national party is an unrecognized party. The Election Commission (EC) confers the status of state party on any party that meets certain thresholds in terms of votes received and seats won in an election. A state party acquires monopoly on the use of its party symbol in the state. A party qualifying as state party in four states gets the national status and then has the monopoly over the use of its election symbol over the entire country. It is not unusual for parties to lose the national status if they lose the qualifications for it.

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with the second most seats ends up in the opposition, state parties, which together

account for approximately 30 percent of the seats acquire great importance.

Table 1: Broad Results of the National Elections in 1999, 2004 and 2009

Party 1999 2004 2009

National Parties 369 364 376

Indian National Congress 114 145 206

Bharatiya Janata Party 182 138 116

Bahujan Samaj Party 14 19 21

Nationalist Congress Party 9 9

Communist Party of India 4 10 4

Communist Party of India (Marxist) 33 43 16

Rashtriya Janata Dal 24 4

State Parties (E.g., DMK, TDP, SP, TC) 158 159 146

SP

36 23

JD (U)

8 20

AITC

2 19

DMK

16 18

BJD

11 14

Shiv Sena

12 11

AIADMK

0 9

TDP

5 6

JD(S)

4 3

Other (unrecognized) Parties 10 15 12

Independent candidates 6 5 9

TOTAL 543 543 543

Led by the Bhartiya Janata Party (BJP), the National Democratic Alliance (NDA)

had ruled from 1999 to 2004. Counting on its popularity at the time, it called for an early

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election. But, the BJP suffered major losses shrinking its seats from 182 to 138, whereas

the INC, the Congress Party, improved its tally significantly from 114 to 145 seats,

though still well short of the 272 seats necessary to form a government.12 But remarkably,

it was successful in cobbling together a coalition that came to be known as the United

Progressive Alliance (UPA). The UPA government successfully served its entire term

until 2009. At one level, it could be argued that neither the decline in the seats held by the

BJP from 182 to 138 nor the rise in the seats held by the Congress from 114 to 145

represented a major shift away from the incumbent towards the opposition. Yet, given the

expectations of a clear mandate in favor of a very popular Prime Minister, the media

uniformly described the outcome as a decisive vote against the incumbents.

The 2009 national election was different from the 2004 election in one fundamental

sense: it returned the main ruling party, the Congress party, INC, to power with a larger

number of seats as well as with a larger victory margin. Beating even the most optimistic

predictions, the Congress increased its tally yet again from 145 to an impressive 206

seats. The Marxist Communist Party suffered the worst losses shrinking from 43 to 16

seats. The BJP also declined from 138 to 116 seats. Thus between 1999 and 2009, the

Congress and the BJP had more or less exchanged their positions.

Among the national parties, the Marxist Communist Party of India, the Communist

party of India, and the Rashtriya Janada Dal (RJD) suffered the largest losses besides the

BJP. RJD even lost its status as a national party after the elections. Among the state

parties, Samajwadi Party suffered the largest losses. Those making major gains other than

the Congress were the Congress ally in West Bengal All India Trinamool Congress and

12 Virmani (2004) offers an analysis of the voter’s behavior in the 2004 election.

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opposition parties JD (U) and AIADMK. The other myriad state parties and unrecognized

parties broadly maintained their positions.

One immediate reaction to the results in the press was that incumbency had helped

rather than hurt in this election, though some observers did question this conclusion.

However our observation is that rather than a simple incumbency factor relevant at the

central government level the picture seems to be more nuanced, and one in which the

incumbency seems more relevant at the state government level and it is the interplay

between state incumbency and economic growth that seems to be a determining factor in

election outcomes. This idea is related to the Bhagwati and Panagariya (2004) hypothesis

that the electorate rewarded the ruling party in a performing state while punishing that in

a non-performing state. And as observed by Panagariya (2009) the outcome in the 2009

election seems consistent with this idea, e.g. he points out that the national incumbent, the

Congress party, could win only nine out of 72 seats in the states of Bihar, Orissa and

Chhattisgarh, which had performing non-Congress governments. On the other hand,

Delhi and Andhra Pradesh had performing Congress chief ministers and the party

respectively bagged seven out of seven and 33 out of 42 seats in those two states. In

Rajasthan, the Congress had trounced out an unpopular BJP chief minister less than six

months prior to the national elections. In the national election, it went on to win 20 out of

25 seats in that state. We investigate these ideas systematically in the rest of this paper.

5. Characteristics of the Constituencies

Table 2 shows the salient features of the constituencies based on all constituencies in

the country. Because these features hardly move when we consider only the 19 states on

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which our regression analysis focuses, we do not show them separately for these latter

states.

A total of 8,071 candidates contested the 2009 election. Of these, as many as 3,825 or

47.4 percent were independent, another 30 percent affiliated with the national or regional

parties and the rest belonged to the unrecognized parties. In the 2009 election, in all 372

parties fielded one or more candidates. Party affiliations in general, especially with a

national or state party, mattered most: candidates with a party affiliation accounted for

more than 98 percent of the top four candidates and for the majority of the winning

candidates. 534 winning candidates out of a maximum possible of 543 had some party

affiliation. Only nine winning candidates had contested as independents.

Table 2: Description of the Constituencies across All States

Average Minimum Maximum

Number of Voters 1,319,916 45,981 2,343,012

Number of Candidates 15 3 43

Voter Turnout (%) 59.4 25.6 90.4

Votes obtained by the Top Candidate (%) 43.9 21.3 78.8

Votes obtained by the Second Candidate (%) 34.3 8.7 48.7

Victory Margin 9.7 .04 70.1

Votes Obtained by the top four candidates (%) 93.8 62.6 100

The average number of candidates per constituency was 15 with the maximum and

minimum number of candidates in any constituency being 43 and 3, respectively.

Remarkably, as the latter figure indicates, there was not a single constituency with direct

election between two candidates. Countrywide, 59.4 percent of the voters turned up to

vote. The maximum turnout was 90.4 percent (in Tamluk constituency in West Bengal)

and the minimum 25.6 percent (in Srinagar constituency in Jammu and Kashmir).

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Constituencies near the higher limit were in West Bengal followed by the North Eastern

and Southern states (Andhra Pradesh, Kerala, Tamil Nadu). Those near the lower end

were in the states of Jammu & Kashmir, Bihar, Uttar Pradesh and Rajasthan.

On average, the winning candidates secured about 44 percent of the votes casted, and

the second candidate from top obtained about 34 percent of the total votes. Ms Sushma

Swaraj of the BJP won with the highest proportionate majority in Vidisha constituency of

the Madhya Pradesh, claiming 78.8 percent of the total votes casted. The simple average

of the percentage point victory margins across all constituencies was 9.7 percent and the

margins ranged from 0.04 to 70 percentage points.

Figure 2: Percentage Votes Obtained by Candidates at Different Ranks

02

04

06

08

0P

erc

ent o

f Vo

tes

Rec

eiv

ed

0 10 20 30 40Candidates ranked, 1=winner

The top four candidates summing to 2,170 out of a total of 8,071 candidates

accounted for the bulk of the votes polled in most constituencies. In aggregate, these

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candidates accounted for more than 90 percent of the total votes polled. This can be

gleaned from the fact that the density of votes in Figure 2 is heavily concentrated in the

first four candidates. In view of this distribution of votes polled, we will frequently limit

the sample to top four candidates in our regressions in Section 7.

6. Characteristics of the Candidates

We now turn to a consideration of some key characteristics of the candidates relevant

to our analysis. These relate to: wealth, education, criminal cases distinguished by the

seriousness of the charges, gender, and incumbency status. We provide the data for all

candidates, the top four candidates the victorious candidates. Because the average hardly

move between all states and 19 largest states, in the following, we confine our

presentation to the former.

6.1. Wealth

Table 3 provides the distribution of candidates by wealth across five different wealth

categories. For each wealth category, column IV shows the percentage of candidates in

the top four candidates and column V of those winning the election. Two features of the

table stand out. First, candidates from all wealth categories are able to participate in

elections and make it to the list of the top four candidates; nearly half of the top four

candidates come from the lowest wealth category of 5 million rupees or less. The system

does seem to offer an opportunity to run for election without regard to wealth status.

Second, the unconditional probability of victory rapidly rises with wealth: while ¾ of the

candidates belong to the bottom two wealth categories, only a little more than a quarter of

the elected candidates come from the latter. Alternatively stated, 56 percent of the

19

winning candidates possess at least INR 10 million in declared wealth (and perhaps much

more in reality). The contrast is brought out most sharply by a comparison of

unconditional probability of victory of a candidate in the highest wealth category (6.7

percent) to that of the lowest wealth category (0.4). It bears cautioning, of course, that no

causal relationship between wealth and election outcome can be drawn from these data.

Wealth can very well be positively correlated with other attributes defining a good

candidate in the eyes of the electorate.

Table 3: Distribution of Contesting and Winning Candidates According to Wealth (Candidates in All Indian States) Wealth Category

Wealth (rupees million)

Number of Candidates (% of total)

Number of top Four

Candidates

Number of Candidates Winning

Probability of Being in

the Top Four

Candidates

Probability of Victory

I II III IV:

(II/I)x100

V:

(III/1)x100

1 0-0.5 3,176 (39%) 274 14 8.6 0.4

2 0.5-5 2,835 (35%) 642 134 22.6 4.7

3 5-9 700 (8.7%) 329 89 47 12.7

4 9-50 896 (11%) 629 194 70.2 21.7

5 50-higher 464 (5.8%) 296 112 63.8 24.1

Total All wealth categories

8071 2170 543 26.9 6.7

6.2. Education

Next, we consider the distribution of candidates by education level. Once again, we

identify five education levels, the lowest one being no formal education and the highest

one a post-graduate or higher or a technical degree. Table 4 reports the frequency

distribution and unconditional probabilities of being in the top four and the winning

20

candidate. Three features of the table are noteworthy. First, contrary to the common

impression, most candidates contesting elections have some formal education. Indeed, the

vast majority of those contesting have at least gone through the middle school.

Table 4: Distribution of Contesting and Winning Candidates According to the Level of Education (Candidates in All Indian States)

Education Education level

Number of

candidates

Number of top Four candidates

Number of winners

Probability of being in

top 4

Probability of victory

Category I II III IV: (II/I)x100

V: (III/1)x100

0 No Formal Education 134 5 0 3.7 0

1 Up to Class V 964 106 15 11 1.6

2 Middle or High School

2,665 495 104 18.6 3.9

3 Undergraduate 1,623 603 157 37.2 9.7

4 Post Graduate or higher or technical

1,984 875 260 44.1 13.1

Total All education levels 7370 2084 536 28.3 7.3

The second point to note from Table 4 is that while the proportion of those with an

undergraduate or higher degree is approximately half among those contesting, it is more

than 80 percent among those winning. Four out of every five members in the 2009 Lok

Sabha boast of an undergraduate or higher degree. At the other extreme, while

approximately 100 candidates with no formal education contested elections, reflecting the

participatory nature of India’s democracy, none actually won.

Finally, the unconditional probability of getting elected consistently rises with the

education level. The biggest jump takes place as we move from high school to a college

degree. We remind, however, that as in the case of wealth, this fact need not reflect

21

causation if education is correlated with other factors that make a candidate attractive to

the electorate.

6.3. Criminal Cases

Perhaps the most interesting characteristic relates to criminal cases pending against

the contesting and winning candidates. Table 5 documents the relevant data. In

constructing the table, we identify five categories based on the number of pending cases

against a candidate. Two features of the table stand out. First, a significant number of

candidates—approximately 14 percent—have criminal cases pending against them; and

even a larger percentage of elected members of Lok Sabha—approximately 30 percent—

have one or more criminal cases registered against them. Even if we exclude the

candidates with just one case since the prospects of frivolous cases are high against those

in politics, more than 80 current members of Lok Sabha, accounting for approximately 15

percent of all members, have two or more criminal cases pending against them. Second,

somewhat disconcertingly, the within group probability of victory is higher for the

candidates with a large number of cases pending against them.

We note that closer examination leads to a more nuanced picture from the one

emerging from the aggregate data shown in Table 5. The criminal charges against the

candidates range from the benign such as participation in rallies declared unlawful to

more serious ones such as murder. To understand the true picture, we must disaggregate

the data further. To economize on space, we relegate this task to an appendix available

upon request from the authors. Here we simply note that the probability of a serious

crime by a contestant rises with the number of criminal cases registered against him or

her. Whereas only 40 percent of the candidates with one case registered against them had

22

been accused of a serious crime, nearly 90 percent of those with 10 or more pending

cases had one or more serious criminal charges against them.

Table 5: Distribution of Contesting and Winning Candidates According to Criminal Cases (Candidates in All Indian states) Crime Category

Number of Criminal

Cases

Number of Candidates

Number of top

Candidates

Number of Winners

Probability of Being in Top 4 candidates

Probability of Victory

I II III IV: (II/I)x100 V: (III/1)x100

0 0 6,894 1,578 381 22.9 5.5

1 1 627 286 76 45.6 12.1

2 2 to 4 392 212 59 54.1 15.1

3 5 to 9 92 61 16 66.3 17.4

4 >10 44 30 11 68.2 25

Total All crime categories

8049 2167 543 26.9 6.7

Detailed examination also reveals shows some concentration of criminal cases against

candidates by state. The state that tops the chart is Bihar with more than a quarter of the

candidates having at least one criminal case and more than 17 percent of the candidates

with at least one serious charge against them. Other states, which exhibit large

proportions of candidates with criminal cases, include Jharkhand, Orissa, Uttar Pradesh,

Gujarat, West Bengal and Maharashtra. We have an interesting case in Kerala where 22

percent of the candidates have criminal cases registered against them but the vast

majority of them involve benign charges such as participation in demonstration; in only a

third of the cases involve serious crimes.

23

6.4. Distribution of Candidates by Gender

Table 6 reports the gender distribution of contesting and victorious candidates. The

data show the expected pattern. Much fewer women than men contest election. This also

translates in many more male members in the Lok Sabha. One mildly interesting feature

is that the unconditional probability of a woman winning the lection is higher than that of

a man.

Table 6: Gender Composition of Contesting and Winning Candidates (All States) Gender Total

Candidates Top Four

Candidates Number of Winning

Candidates

Probability of being in Top

Four

Probability of Victory

I II III IV: (II/I)x100 V: (III/I)x100

Women 556 183 58 32.9 10.4

Men 7,515 1,987 485 26.4 6.5

Total 8071 2170 543 26.9 6.7

6.5. Incumbents and Non-incumbents Compared

We may now compare the incumbent and non-incumbent candidates within the

populations of all, top four and victorious candidates. As table 7 shows, while non-

incumbent candidates far outnumber incumbent ones, virtually all of the latter are among

the top four. With 15 candidates per constituency contesting election on average, it

should be no surprise that even if half of the incumbents were voted out, the

unconditional probability of their victory relative to non-incumbents would be very high.

Therefore, losses to a large number of incumbents are quite consistent with the

incumbents having a strong showing in a statistical sense. In a similar vein, even as the

main parties such as the Congress and the BJP might experience a decline in their tally of

24

seats, the statistical probability of their candidates winning would still remain very high

relative to the rest of the main parties taken together. Thus in our regression we include a

control variable representing the size of the party.

Table 7: Incumbents among all contestants, top four and winners (All States)

Incumbency Status

Total Candidates

Top Four Candidates

Number of Winning

Candidates

Probability of being in top

four

Probability of victory

I II III IV: (II/I)x100 V: (III/I)x100

Incumbents 387 376 184 97.2 47.5

Non incumbents 7684 1,794 359 23.3 4.7

Total 8,071 2170 543 26.9 6.7

Table 8: Average of the Characteristics Across Various Candidates (All States) Characteristics All Candidates Top four

candidates Winning

candidates

Age 46 51 53

Wealth Category

(Average wealth in rupees)

2.1

(17 mln INR)

3

(41 mln INR)

5

(59 mln INR)

Criminal Record (probability) 14 27 30

Serious Crime (Probability) 7.4 13 13.8

Member, national party (probability) 20 60 69

Member, state party (probability) 9 19 27

Male (probability) 93 92 89

Education (category) 2.6 3.1 3.2

Incumbent (probability) 4.8 17.3 34

Next, in Table 8, we provide the average of each characteristic across all candidates

and the winning candidates. If we could construct a winning candidate with these

average characteristics, he would be a wealthy male (with mean assets worth 59 million

25

rupees and median assets worth 12 million rupees) in his mid 50s with at least an

undergraduate degree. He would come from one of the main political parties. There is a

30 percent chance that he would have at least one criminal case against him and a 15

percent chance that he will have 2 or more criminal cases against him, and a 14 percent

chance that the case would involve a serious crime. There is also 34 percent probability

that he had served as an MP in the previous parliament.

7. Regression Model and Results

We now turn to a quantitative analysis of the election results using data at the

candidate level. We first outline the empirical model we employ followed by the

discussion of various implementation issues such as the selection of states, definition of

incumbency and the measurement of economic performance and candidate

characteristics.

7.1. The Empirical Model

Because we only observe the number and percentage of votes obtained by each

candidate and the outcome of the elections rather than the direct behavior of the voters,

we use a latent variable approach to model the voter’s decision to vote. We include three

kinds of variables in the model: candidate specific characteristics, party specific attributes

including their ideologies and performance of the incumbent candidates and their parties.

Consider each of these in turn. First, the voters’ voting behavior may have preference

for certain types of candidates, leading them to vote on the basis of their characteristics;

e.g., some voters may prefer a more educated candidate over a less educated one, or for a

woman candidate, some voters may not want to vote for a candidate who has criminal or

26

serious criminal charges against him, etc. Wealth can be an important factor affecting the

voter’s behavior as it may directly have a bearing on the voter’s preference for a certain

candidate, and can perhaps also be used, through general or targeted election expenditure

to influence the voter’s behavior.

Second, the voters may have some ideological leanings, leading them to favor one

party over the other parties. To the extent that these preferences may differ across states

and parties, we control for state fixed effects and party fixed effects in the regressions. In

some specifications we control for fixed effects varying over state-party.

Finally, besides the observable characteristics of the candidates, and their ideological

preferences, voters may also infer about the competence of the candidates or the parties

by examining the performance record of the incumbent candidates or their parties.

However, there is very little information by way of judging the past performance of the

incumbent candidates. In the absence of more direct information, the voters might draw

inferences about the competence of a candidate through observed attributes such as

experience, gender, education, wealth, party affiliation, criminal record etc., the variables

which we include in the model.13 Since judging the competence of an individual

candidate is difficult, the voters might take into account the performance of the party at

the state as a proxy for the collective competence of the party. We measure incumbent

party’s performance by the growth record of the state.

13 In principle, an incumbent candidate’s performance can be judged by variables such as the presence in parliament, the participation in debates through the number of questions asked, frequency of visits to the constituency and efforts to assist the constituency members through government funded projects and programs. One could also look at the amount spent and the quality of work done using the funds available under the MPLAD scheme. But these data are difficult to come by. Furthermore, a casual look at the data on the disbursements of funds under MPLAD suggests that there is little variation in the amount spent across the members of parliament. The number of projects undertaken runs into hundreds, and again one cannot judge their quality based on the description of these projects.

27

Formally, we represent the probability of winning elections as follows:

Prob(Yc,s,p=1)=G(Candidate characteristics, Party characteristics, Candidate and state

performance)

Or, more specifically,

(1) Prob(Yc,s,p=1)=α + βcXc + βpXp + βsXs

+ γcIncumbencyc + γsIncumbencys + γnIncumbencyn +

δcIncumbencyc*Economic Performance+ δsIncumbencys*Economic Performance

+ δnIncumbencyn*Economic Performance

Here Yc,s,p refers to the election outcome of candidate c, belonging to party p, who is

contesting from state s. It is a binary variable taking the value of 1 in case of victory and

0 in case of defeat. Xc is a vector of candidate-specific variables such as wealth,

education, gender, number of criminal cases pending and candidate level incumbency. Xp,

likewise, is a vector representing party-specific variables, and Xs, is a vector representing

state specific variables. Besides controlling for selected variables for state or party

specific effects, we include state fixed effects and party fixed effects in the regressions.

The next three variables measure the effect of incumbency at candidate, state, or

national level, respectively; and the final three variables measure the state’s economic

performance has on the probability of victory of the incumbent candidate, the candidate

of the national incumbent party and the candidate of the state incumbent party.

The coefficient δs measures the advantage an incumbent enjoys over non-

incumbents in a fast growing state relative to a reference unit of analysis. For example,

suppose we represent economic performance by the difference between the annual

growth rate of the state and the national average growth rate. Then the coefficient

28

represents the advantage the candidate of the incumbent party enjoys over the candidates

of non-incumbent parties in a state exhibiting 1 percentage point higher growth than the

national growth rate relative to that in a state exhibiting the national average growth rate.

Alternatively, we could define the economic performance by a dummy variable such that

it takes a value of 1 for the states exhibiting above-median growth and 0 for those with

below-median growth. In this case, the coefficient δs represents the advantage the

candidates of the incumbent party enjoy over those of non-incumbent parties in a state

exhibiting above-median growth relative to that enjoyed by the candidates of the

incumbent party over those of non-incumbent parties in a state exhibiting below-median

growth.

We estimate the model using logit specification and report the marginal effects of

the variables considered. In robustness tests we also compare the results by estimating

our specifications using an Ordinary Least Squares model as well as a Probit

specification. We also estimate a model using the proportion of votes received by each

candidate as the dependent variable through ordinary least squares, and another

specification using the tobit model in which the dependent variable equals the victory

margin (or percentage of votes received) for the winning candidate and 0 for other

candidates.

Below, we first discuss the sample, data sources used, details of variables used in

the regressions, and how these have been constructed, and then we present our

regressions results.

29

7.2. Selection of States

Since by definition, the union territories do not have territory-level governments,

we cannot define the variable representing the state-level incumbency, and therefore we

exclude them from our regressions. In addition, we exclude Jammu and Kashmir, and the

seven northeastern states (including Assam) and Sikkim. These states have very small

number of national electoral constituencies and, more importantly from the viewpoint of

election outcomes, have a very strong presence of the central government. Finally, due to

multiple turnovers of the government, we are unable to determine the incumbent

government in Karnataka. These exclusions limit the sample to 19 states: Andhra

Pradesh, Bihar, Chhattisgarh, Goa, Gujarat, Haryana, Himachal Pradesh, Jharkhand,

Kerala, Madhya Pradesh, Maharashtra, National Capital Territory Of Delhi, Orissa,

Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttarakhand, West Bengal.

For reasons of consistency, we work with this sample of 19 states throughout the

paper. The sample includes 478 constituencies and 90 percent of the eligible voters. Of

the included constituencies, 364 are in general categories, 78 reserved for the scheduled

caste candidates and 36 for the scheduled tribe candidates. The sample consists of 7,268

candidates, but there are some missing observations due to the unavailability of data for

education and in a few cases on criminal cases, or wealth. In general we are able to

include data for approximately 6,600 observations in regressions with all candidates and

for about 1,800 observations in regressions restricted to top four candidates. The number

of observations sometimes varies across regressions due to missing values of one or more

variables. We also check the robustness of our results by dropping education, or all the

30

candidate characteristics, from the sample and estimating the regressions with a larger

number of observations.14

7.3. Defining Incumbency

Incumbency in India can be defined not only for the sitting member of the Lok Sabha,

but also for the party in power at the center that in the state in which the constituency is

located.15 In the present paper we analyze the roles of all three levels of incumbencies in

affecting the election outcomes.

Taking the incumbency at the level of the state first, we define variable

Incumencys such that it takes a value of 1 in the case of a candidate belonging to the

incumbent party in the state in which his or her constituency is located and of 0 in the

case of all other candidates. The incumbent party, in turn, is defined as the main ruling

party (or two main ruling parties when power is shared) in 2007 and in at least two

consecutive preceding years. If there was an election for the state legislative assembly in

2008 or 2009 and the party ruling until 2007 lost this election, it was still considered the

incumbent party for purposes of the national elections held in April-May 2009. The

underlying logic is that an electorate would treat the party that was in government for

several years prior to 2009 responsible for the policies and performance of the state rather

than the party that took over as the government in the year just before the general

election.

14 In our sample of 19 states we have included Goa, which is a small state with only two parliamentary seats though it does not have a strong central presence. Sticking to the size criterion for inclusion in the sample, we could drop Goa but doing so does not alter any of our key results. For robustness tests, we also estimate our regressions for all states. Our results hold with this larger sample with 7306 observations. 15 Indeed a large part of the literature has looked at the incumbency at the candidate level or the central government level. The main papers relating the central election outcomes to state-level incumbency have been discussed in our review above.

31

We define incumbency at the national level, Incumbencyn, by a simple dummy

variable that takes the value of 1 for candidates belonging to a members of the UPA (or

sometimes just the Congress), which governed during at the center 2004-09, and 0 for all

other candidates. We also consider a continuous analogue of the variable representing it

by the percentage of seats held by the party of the candidate in the outgoing parliament in

the state in which the constituency is located. For example, if the BJP held 80 percent of

the seats from Rajasthan in the outgoing parliament, the variable would take the value 80

for the BJP candidates contesting from constituencies in Rajasthan. This variable helps us

examine whether a party’s performance in 2004 elections in a given state can predict the

performance in 2009 elections.

To construct the incumbency at the candidate level, Incumbencyc, we use the list

of the members of parliament in the 2004 Lok Sabha from the parliament’s website. This

seemingly simple task of matching the names of the members of parliament in the

candidate’s lists for 2009 turns out to be extremely tedious. As other authors working

with the Indian elections data have reported earlier, names are often recorded in different

ways across databases and have to be matched manually. The task could have been made

simpler if the incumbents contested elections from the same parliamentary constituency

in 2004 and 2009 elections. However, due to the delimitation exercise carried out in

2008, the names of the constituencies have changed. In addition, the parliament’s website

retains the names of all the members of parliament who were elected to the Lok Sabha

even if the seat was later either given up, and a by-election was held.

After we matched the names of the sitting members of the parliament in the

candidates list, in order to make sure that we have not omitted any names of the

32

incumbents we carried out a detailed search in the online news items for each member of

the parliament whose name we could not track down in the candidate database for 2009

election. In the process we could also document the reasons for why some of the

incumbents did not contest the elections in 2009 (see Appendix C).

Besides the incumbency status at the time of the election, the length of

incumbency at all levels may also matter for the election outcome. The length of

incumbency can possibly affect the election outcome in either direction. A longer

duration may allow the candidate or the party to deliver on the promises more fully but it

may also expose their inability to keep the many inconsistent promises. A sufficiently

long duration may also give rise to an “incumbency fatigue” whereby voters want a

change and hand defeat to even performing incumbent candidates or parties. We have not

yet included the duration of incumbency in our analysis but plan to put these data

together and include it in our future work.

7.4. Defining the Economic Performance

While economic performance can be defined in different ways, we use the growth

rate of state domestic product as a measure of economic performance of the state

government. We use three alternative definitions for this variable. Under the first

definition, we define it as the differential growth rate of the state as compared to the

national growth rate, and specifically, set this variable equal to the average growth rate in

the state from 2004-05 to 2008-09 minus the average national growth rate over the same

period. The variable takes a positive value for states with growth rates above the national

average and a negative value for states below the national average. As an alternative, we

33

use the average growth rate differential in only three years prior to the elections i.e. in

2006-07 to 2008-09.

Under the second definition, we divide the states into two groups: those

experiencing above median growth and those experiencing below median growth. The

broken line in Figure 3 shows this division. In this case, the variable economic

performance takes the form of a dummy variable that takes a value of 1 for states with

above-median growth and 0 for those with below-median growth.

Finally, we arrange the states according to declining average rates of growth and

then divide them into three roughly equal-size groups with high, medium and low

average growth rates. The solid lines in Figure 3 demarcate these groups. Under this third

definition, we set the economic performance variable equal to 1 for states in the highest-

growth group and 0 for the lowest growth group, with the middle states dropped from the

sample. Thus Delhi, Bihar, Goa, Gujarat, Haryana and Chhattisgarh are in high growth

group; and Himachal Pradesh, West Bengal, Rajasthan, Uttar Pradesh, Punjab, and

Madhya Pradesh are in the low growth group.

There can be several potential concerns with our definition of economic

performance. First, instead of measuring performance at the state level, perhaps we

should measure performance at the constituency level. Unfortunately it is difficult to

measure economic performance at the constituency level, and no such data exist to our

knowledge.

A second concern is that economic growth alone is perhaps not a sufficient

measure of the performance of the incumbent state government, and one should account

for the performance record on other indicators, such as literacy, health, infrastructure,

34

equality, governance, fiscal deficits and development expenditures. Again lack of data is

the main constraint on the inclusion of these alternative measures of performance. Our

view is that since many of these other measures of performance are likely to be highly

correlated with growth, they are likely to yield similar results and at the same time it

would be difficult to disentangle the effects of growth and, say, infrastructure. But we

admit that what we are attributing to growth here is perhaps attributed to a broader

measure of achievements than to just growth.16

Figure 3: Difference between the Average Growth Rate of State Domestic Product and the GDP Growth Rate (2004-2008)

-4 -2 0 2 4

MP

PJ

UP

RJ

WB

HP

JD

TN

MH

AP

UL

KL

CT

HY

OR

GJ

GA

BH

DL

16 To some degree, this omitted variables concern is alleviated by the fact that our results are stronger when we divide the states into top tercile and bottom tercile according to growth rates. It may be hypothesized that the states differ much more in terms of growth than other performance indicators across these two groups.

35

The last concern could be related to the way we measure the growth performance.

We define it relative to the national average, but one should perhaps look at the growth

turnaround within the state. So rather than looking at the absolute growth rate the voter

may judge the performance of the incumbent by comparing the growth rate under the

incumbent with the state’s past growth performance. While we plan to pursue these

various alternatives in our future work subject to the data availability, they are beyond the

scope of the present paper.

7.5. Interaction between Performance and Incumbency

We multiply the incumbency variables with the economic growth variables to

calculate the interaction between incumbency and performance. A positive value of the

coefficient implies that the incumbent candidate or candidate of the incumbent party is

rewarded relative to the non-incumbent candidate or the candidates of non-incumbent

parties, as the state grows faster or slower than the national average.

7.6. Candidate Specific Variables

For the candidate level variables, we control for age, gender, education, wealth, and

criminal record of each candidate, besides the incumbency status that we have already

described.

7.7. State Specific and Party Specific Variables and Fixed Effects

To account for state specific attributes such as literacy, main ideology of its voters, or

state specific incidents such as a natural calamity, which might affect the election

outcomes, we include state specific fixed effects in all our specifications. Similarly, to

36

account for party level factors such sympathy wave or outrage due to corruption charges

or squabbles within the party, we include party fixed effects.

In addition, we also check the robustness of our results to including fixed effects

that differ by state-party pair to account for the ideological leanings of voters for specific

parties in specific states, or other factors that might vary across state-party pairs.

7.8. Size of the Party

The size of a party may also be relevant to the election outcome. On one hand, larger

parties enjoy a reputation advantage but on the other they are also at a disadvantage in

that they field a large number of candidates so that their most effective campaigners get

spread thinly. To allow for these possibilities, we include a variable representing the size

of the party in our regressions. We measure party size by the percentage of total

constituencies in which the party contests election. Both INC and BJP have a value close

to 90 for the size variable, as they contested elections from 440 and 433 seats

respectively; and BSP being the largest party having contested elections from 500 seats in

2009.

This completes the description of our empirical model and we can now present

and discuss our results.

7.9. Principal Regression Results

We present our regression results in the following order. First we estimate regressions

only with candidate characteristics, as a natural sequence to calculating the bilateral

correlations that we presented in Section V to see which characteristics remain significant

when we include all of them together in the regressions. Then we include the rest of our

37

variables—the state and party fixed effects, incumbency variables, and the interaction of

the growth and incumbency along with the candidate specific information. Finally, we

present the variations in our key regressions by way of robustness tests.

Results when we include just the candidate characteristics in the regressions are in

Table 9. In Column I in the table we include age, gender, and index values for wealth,

incumbency status, and criminal record, as defined and discussed in Section 5. We find

that age, gender, education, wealth, criminal cases, and incumbency status are all

positively correlated with the probability of a candidate being elected. We also separately

include a dummy variable to indicate whether the candidate has any serious criminal

charges against him, but its coefficient is insignificant.

In order to account for the possibility that the effects of these variables might be

non-linear and vary across various index values of the variables we have defined, we

include separate dummies for different categories of these variables in Column II of the

table. We notice that education and wealth status of the candidates have a much larger

effect on the probability of getting elected at higher levels of these variables. Thus, while

moving to a wealth bracket of 3 (average wealth is 5-10 million INR) increases the

probability of being elected by 4 percent, the candidates who belong to the highest wealth

bracket (average wealth is more than 50 million INR) have a 19 percent higher

probability of getting elected, over the people who belong to the lowest bracket.

The table allows us to make three additional observations. First, we find that the

criminal record and wealth variables are highly correlated, and more so at the upper end

of the distribution of criminal cases and wealth, and that is perhaps the reason, why when

we include wealth and criminal cases together, the latter is insignificant. Thus in column

38

III when we include only the criminal cases index and do not include wealth, we find its

coefficient to be significant.17

Second we note that besides the individual characteristics what also seems to be

very important in increasing the probability of being elected is to have an affiliation with

one of the major political parties. Thus, when we include separate dummies for national

and state parties in regressions from column IV onwards, we find that these have

numerically large and significant coefficients. We also notice that when we include the

dummies for major parties, the coefficients of wealth, education and criminal record

variables become smaller, implying that the larger parties nominate candidates for

elections who are wealthier, are more educated, and have more criminal records.

Our third observation is that an incumbent candidate has a higher probability of

winning than any other candidate. The effect of incumbency is smaller when we control

for party fixed effects, reflecting that a large number of the incumbents are from a

national or state party, thus one cannot really disentangle the party effect from the

incumbency effect.

Table 9: Election Outcome and Candidate Level Characteristics

I II III IV V VI

Age 0.0*** 0.0*** 0.0*** 0 0 0

[3.82] [2.62] [7.57] [0.59] [0.71] [-0.13]

Gender .02** .02** .04*** .01** .01** .08*

[2.52] [2.56] [3.30] [2.12] [2.19] [1.94]

17 While the answer to the question why are the candidates with a large number of criminal cases or serious criminal cases elected, is beyond the scope of this paper, our analysis (not reported here) shows that the candidates with more criminal cases are wealthier, and are nominated by one of the main political parties. The wealth possibly allows them to buy patronage in elections and in the absence of conviction from the judicial courts, the party nomination works like an endorsement of their innocence. Many of the candidates with criminal cases are also sitting MPs, which also seems to contribute to the probability of these candidates being elected, not just due to name recognition but perhaps also because they are able to use the official machinery to their advantage.

39

Education Index 0.01***

[6.99]

Wealth Index 0.02***

[12.93]

Criminal Cases Index 0.01***

[3.73]

Serious Criminal Case, Dummy 0 0 0 0 0 0.01

[-0.72] [-0.63] [-0.36] [-0.45] [-0.77] [0.16]

Incumbent MP, Dummy .14*** .10*** 0.23*** 0.02*** 0.02*** 0.18***

[7.07] [5.96] [9.09] [3.98] [3.99] [6.16]

Education, Index Value 3 0.02* 0.05*** 0 0.02

[1.88] [2.95] [0.88] [0.35]

Education, Index Value 3 0.04*** 0.12*** 0.01 0.05

[2.66] [4.05] [1.53] [0.83]

Education, Index Value 3 0.04*** 0.14*** 0.01* 0.07

[3.07] [4.88] [1.93] [1.17]

Wealth, Index Value 2 -0.01 0 0 -0.08

[-0.57] [-0.91] [-0.57] [-1.06]

Wealth, Index Value 3 0.04** 0.01 0.01* 0.14

[2.14] [1.57] [1.90] [1.42]

Wealth, Index Value 4 0.12*** 0.03** 0.03*** 0.17**

[3.48] [2.39] [2.66] [2.01]

Wealth, Index Value 5 0.19*** 0.04*** 0.05*** 0.20***

[4.17] [2.73] [2.96] [3.01]

Criminal Cases, Index 2 0.01* 0.03*** 0 0 -0.01

[1.84] [2.77] [0.70] [0.87] [-0.20]

Criminal Cases, Index 3 0.02** 0.06*** 0 0 0

[2.21] [3.15] [1.14] [1.22] [0.02]

Criminal Cases, Index 4 .02 .08** 0 0 -0.02

[1.41] [2.08] [0.65] [0.57] [-0.39]

Criminal Cases, Index 5 .04 .14** .01 .01 .03

[1.45] [2.07] [1.04] [1.01] [0.31]

National Party Dummy .10*** .10*** .24***

[7.48] [7.59] [7.86]

State Party Dummy .17*** .16*** .44***

[6.93] [6.95] [7.92]

Observations 6,601 6,601 6,619 6,601 7,133 1,825

Psuedo R2

*, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent levels respectively.

We try out two more variations on these regressions. First, as noted earlier, there

are some missing observations for education. Therefore, we drop the education variable

40

in column V to see if the coefficients of the rest of the variables change with the larger

number of observations. We find that the results remain similar with the larger data set

and with the exclusion of education.

We noted earlier that the top four candidates got on average 90 percent of the

votes in the elections, and in a way the real contest was among these four candidates,

majority of which contested the elections on a party’s ticket.18 In the last variation we just

take the sample of the top four candidates to see whether the candidate characteristics are

more important in choosing the winner from the top four candidates, and find that wealth,

incumbency status and party affiliations remain as important factors in determining the

winner from the top four candidates. Education is no longer significant perhaps because

there is less variation in this variable within the top four candidates. In order to reduce

noise in the data, this is our preferred sample in the remaining specifications in the paper,

though we conduct robustness tests with the entire sample as well.

Next we estimate the full specification as described in equation 1 and include the

candidate specific variables, i.e., age, gender, education, wealth, criminal record and

candidate incumbency; individual party fixed effects and state fixed effects; and

incumbency variables at the central ruling party level and for the ruling party in the state.

Our preferred sample is the sample of top four candidates.

The results are in Table 10. Three principal results follow from this table. First,

candidate-level incumbency makes statistically highly significant contribution to victory.

18 We separately also look at what candidate characteristics determine whether the candidate belongs to the top four candidates and find that gender, wealth, education, incumbency status, and party affiliations are all correlated with the probability of making into the top four candidates. Women, wealthier candidates, educated candidates and the candidates with more number of criminal cases, or the candidates with a national or state party affiliation, all have a higher probability of making in to top four candidates. Controlling for the number of criminal cases and other variables, the candidates with serious cases registered against them, have a lower probability of making into top four candidates.

41

Ceteris paribus, regardless of their party affiliation, sitting members of Lok Sabha have

higher probability of victory than the other candidates.

Second, while incumbency measured as the party’s performance in the 2004

general elections at the state level does not have a significant coefficient, consistent with

the general impression, ceteris paribus, membership in the INC (or UPA, not shown)

gave candidates greater advantage than membership in any other party grouping. That is

to say, the INC candidates had an edge over all other candidates controlling for age,

gender, education, wealth, criminal record and candidate- or state-level incumbency.

Finally, incumbency at the state level plays an important role. Holding constant

other factors, a candidate belonging to the state incumbent party had 35-40 percent higher

chance of being elected than the candidates of other parties. However, as we will see

shortly, this positive effect is sharper in the faster growing states and is not obtained in

the bottom one third slowest growing states.

Table 10: Election Outcomes and State Growth-Incumbency Interaction

I II III IV V VI VII

Dummy for INC 0.33*** 0.36*** 0.38*** 0.38*** 0.36*** 0.37*** 0.15*** [8.54] [5.58] [6.10] [6.16] [6.95] [5.78] [4.38]

Age -0.001 -0.001 -0.001 -0.001 -0.002 -0.002 0

[-0.89] [-0.95] [-1.11] [-0.83] [-1.46] [-1.48] [-0.99]

Gender, Dummy=1 if Woman 0.05 0.05 0.06 0.05 0.11** 0.11** 0.01*

[1.36] [1.40] [1.45] [1.27] [2.12] [2.13] [1.94]

Education Index 0.020* 0.018 0.021* 0.014 0.014 0.002**

[1.81] [1.64] [1.89] [1.01] [0.98] [2.04]

Wealth Index .03*** .031*** .03*** .03*** 0.03*** .034*** .004***

[3.59] [3.27] [3.30] [3.34] [2.73] [2.73] [3.27]

Criminal Cases Index 0.01 0.009 0.008 0.009 -0.005 -0.005 0

[0.67] [0.62] [0.55] [0.64] [-0.26] [-0.27] [0.27]

Serious Criminal Case, Dummy 0.02 .024 0.028 0.019 0.04 0.04 0.003

[0.60] [0.59] [0.71] [0.48] [0.71] [0.73] [0.72]

Incumbent MP, Dummy .09*** .10*** .09*** .10*** .099** .097** 0.01*

[3.12] [2.97] [3.04] [2.79] [2.32] [2.27] [1.89]

42

Incumbent in State 0.47*** 0.42*** 0.36*** 0.36*** 0.13 0.09 0.002 [8.52] [6.66] [5.38] [4.92] [1.03] [0.89] [0.29] Seats in 2004 0 0.001 0.001 0 0.002 0.003*** 0.00*** [0.40] [1.37] [1.22] [-0.29] [1.38] [2.62] [3.20] Growth Differential (04-08)*State Incumbent .059*** [2.84] Growth Differential (04-08)*Incumbent -0.002 [-0.16] Growth Differential (04-08)*Seats in 2004 0 [-0.36] Growth Differential (06-08)*State Incumbent .047*** [3.44] Growth Differential (06-08)*Incumbent -0.005 [-0.50] Growth Differential (06-08)*Seats in 2004 0 [-1.30] High Growth State (1/2)*State Incumbent 0.217* [1.74] High Growth State (1/2)*Incumbent -0.012 [-0.25] High Growth State (1/2)*Seats in 2004 0.001 [0.71] High Growth State (1/3)*State Incumbent 0.61*** 0.66*** 0.47** [4.05] [5.73] [2.40] High Growth State (1/3)*Incumbent -0.02 -0.01 -0.001 [-0.30] [-0.12] [-0.33] High Growth State (1/3)*Seats in 2004 0.001 0 [0.64] [0.02] High Growth State (1/3)*INC -0.015 [-0.21] Observations 1,787 1,787 1,787 1,787 1,136 1,136 4,011 Psuedo R2

*, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent levels respectively.

Next we introduce the key variables of interest: interaction between incumbency

at various levels and economic performance. This is done in columns II-IV, using

different definitions of economic performance. In column II we use the variable

Economic Performance in its continuous form and set it equal to the average growth in

43

the state minus the national average growth rate during 2004-2008. For states with

average growth rate below the national average, this performance variable takes a

negative value. The estimated coefficient of the interaction between economic

performance and state incumbency is positive and significant. The value of the

coefficient is 0.059, which says that the advantage in terms of winning probability

enjoyed by the candidates of a state incumbent party over those of non-incumbent parties

is 6 percentage points higher in a state that grows 1 percentage point faster than the

national average relative to the advantage the candidates of an incumbent party enjoy

over those of non-incumbent parties in a state that grows at the national average rate. If

the growth advantage in the state is 2 percentage points, the incumbency advantage rises

by 12 percentage points.

The coefficients of the interaction between candidate level incumbency and

economic performance and between central level ruling party and economic performance

are insignificant, implying that the incumbency at candidate or central party level is not

necessarily rewarded more in better performing states.

In Column III we use the growth differential over a shorter and more recent

period 2006-07 to 2008-09. The estimated coefficient of interest between economic

performance and state incumbency is somewhat smaller at about 5 percent but remains

highly significant. The coefficients of other variables are similar to what we had in the

earlier specification.

In column IV of table 10, we define Economic Performance as a dummy variable,

calling it the “Dummy for high growth states.” As explained earlier, it takes a value of 1

for candidates of incumbent parties in states with above-median growth and zero

44

otherwise. When we interact this dummy with state incumbency we obtain a coefficient

that is positive and significant. The value of the coefficient is 0.217, implying that the

candidates of incumbent parties in states with above median growth on average have an

additional advantage of about 22 percent over those of non-incumbents, than the

incumbents in slow growing states do.

In column V, we go a step further to sharpen the contribution of growth to the

election of the candidates of the state-level incumbent party and now define the

Economic Performance variable as “Dummy for high growth states _1/3,” which takes a

value of 1 for the states with top third rates of growth and 0 for the states in the lowest

growth group; and the states in the middle dropped from the sample. Thus now, through

our interaction variables, we get the effect of incumbency in high-growth group relative

to the low-growth group. The difference is sharper now with the value of the coefficient

of the interaction between the economic performance and state incumbency increasing to

0.61. In other words, the candidates of the incumbent parties in the top third of the states

have on average a 60 percent greater advantage over the candidates of the non-incumbent

parties than the incumbents in the slow growing states do. In addition, the coefficient of

the variable state incumbency is rendered insignificant implying that the incumbents do

not have an advantage over the non-incumbents in slow growing states. Quantitatively,

the effect of being a candidate of the state incumbent party in high growth states dwarfs

the effects of any other factors, and thus can indeed be a determining factor in the

election outcomes.

We do two more variations in the last two columns of table 10. In Column VI, for

the center incumbency and growth interaction we interact the dummy for the Indian

45

National Congress party with the high growth state dummy. Through this variable we can

see if after controlling for other factors INC candidates benefit more in high growth

states. But the coefficient is negative and insignificant answering the question in the

negative. In column VII we use the larger sample of all candidates and not just the top

four candidates. As expected, the coefficients are smaller now, but qualitatively all the

results hold.

7.10. Further Robustness Checks

Our sharpest results are obtained in specification in column V in Table 10, and

this is the specification we use for our robustness tests that follow. First set of checks is

shown in table 11.

First, instead of using a logit model specification, we estimate the specification

with the probit and linear models, respectively in columns I and II. The results are robust

and are similar to the ones in our key specifications, not just qualitatively but also

quantitatively. Since we have missing data for several observations for the education

variable, we drop it from our sample in column III to make sure that our results are not

contaminated by the restrictive sample used; in column IV, we also drop all the other

individual characteristics, and find that our results are robust. This helps us maximize the

number of observations used while also allowing us to test whether the exclusion of the

candidate characteristics affect our key results. The results in column IV are important

because the candidate specific information is available only for one prior election, i.e. for

the 2004 election and this too is only partial with no systematic documentation available.

Our results indicate that even if we estimate the regressions without including these

46

candidate characteristics to compare the results with the earlier elections, it would not be

a serious omission.

Table 11: Robustness Checks

I II III IV V VI

Probit Model

Linear Model

Drop Education

Drop Candidate Specific

Variables

Dependent Variable: percent

votes obtained

Tobit Model

Dummy for INC 0.34*** 0.24*** .37*** 0.39*** 13.5*** 13.0*** [7.17] [7.61] [7.12] [7.99] [11.79] [7.39] Age -0.002 -0.001 -0.001 -0.006 -0.105*

[-1.37] [-1.47] [-1.29] [-0.19] [-1.93] Gender, Dummy=1 if Woman .10** .096** .11** 1.5 3.9**

[2.03] [2.58] [2.25] [1.29] [2.21] Education Index 0.02 0.01 0.94*** 1.03

[1.00] [0.60] [2.67] [1.54] Wealth Index .038*** .028*** .03*** 2.6*** 2.27***

[2.96] [2.60] [2.68] [7.32] [3.86] Criminal Cases Index -0.007 -0.007 0 0.44 -0.32

[-0.32] [-0.39] [0.01] [0.84] [-0.33] Serious Criminal Case, Dummy 0.043 0.035 0.013 0.82 1.13

[0.76] [0.77] [0.28] [0.62] [0.48] Incumbent MP, Dummy 0.11** .132*** .094** .095** 6.28*** 5.11***

[2.39] [3.40] [2.28] [2.32] [4.58] [2.98] Incumbent in State 0.13 0.01 0.11 0.13 6.13*** 4.14 [1.07] [0.20] [0.91] [1.02] [3.62] [0.79] Seats in 2004 0.002 .003*** 0.002 0.002 0.28*** 0.12 [1.47] [2.67] [1.57] [1.55] [9.39] [1.52] High Growth State (1/3)*State Incumbent 0.59*** .615*** .63*** 0.62*** 11.2*** 22.8*** [4.10] [6.57] [4.31] [4.33] [4.59] [3.79] High Growth State (1/3)*Incumbent -0.037 -0.083 -0.015 -0.013 -4.27** -1.53 [-0.58] [-1.28] [-0.26] [-0.21] [-2.11] [-0.52] High Growth State (1/3)*Seats in 2004 0.001 -0.002 0.001 0.001 -0.07* -0.036 [0.33] [-1.48] [0.57] [0.69] [-1.88] [-0.37] Constant -0.02 1.42 [-0.25] [0.63]

47

Observations 1,136 1,175 1,171 1,175 1,175 1175 R-squared 0.317 0.624

The coefficients in column V use the percent of votes obtained by the candidate as

the dependent variable with the sample restricted to the top four candidates. They show

that the candidates of incumbent parties in states obtain 6.2 percentage points larger share

even if not all of them win elections; and the candidates of the incumbent parties in high

growth states secure an additional 11 percentage points more votes. Results in the last

column show that the candidates of the incumbent party win by a larger margin, on

average by a 22 percent larger margin, in the fast growing state, than the margin

incumbents enjoy over the non-incumbents in the slow growing states.

Another robustness check we perform but do not report involves dropping one

state at a time and estimating the regressions for the sample of remaining states, and

confirm that our results are not driven by any one state.

Our final set of regressions appears in Table 12. In this table, we truncate the

sample across states based on growth performance. Column titles in the table clearly

identify each sample. Three observations are worth making with respect to the results in

columns I to IV. First, in the high-growth states, all personal characteristics of candidates

become statistically insignificant, but remain significant in the slow growing states.

Second, the coefficient of the state incumbency is much larger in the high-growth states

(compare the coefficient between columns I and II or those between columns III and IV)

than in the low growth states. Finally, the candidates of the INC have a higher

probability of winning in the slow growing states.

48

Table 12: High and Low Growth States Separately Considered

I High Growth States

Top Four Candidates

II Low Growth

States Top Four

Candidates

III High Growth

States All the

Candidates

IV Low Growth

States Top Four

Candidates Dummy for INC 0.35*** 0.38*** 0.075** 0.24*** [3.77] [5.99] [2.17] [4.74]Age 0.002 -0.003** 0 0

[0.54] [-1.97] [0.83] [-1.56]Gender, Dummy=1 if Woman 0.025 0.146** 0.002 0.023**

[0.25] [2.29] [0.36] [2.04]Education Index 0 0.025 0.001 0.005**

[-0.01] [1.41] [0.40] [2.54]Wealth Index 0.019 0.045*** 0.001 0.008***

[0.68] [2.88] [0.94] [4.26]Criminal Cases Index 0.026 -0.021 0.002 -0.001

[0.63] [-0.76] [0.94] [-0.42]Serious Criminal Case, Dummy -0.07 0.11 -0.004 0.02

[-0.90] [1.29] [-1.27] [1.31]Incumbent MP, Dummy 0.088 0.114** 0.005 0.017**

[1.12] [2.46] [0.92] [2.08]

Incumbent in State 0.65*** 0.34** 0.26** 0.07 [8.69] [2.51] [2.45] [1.39]Seats in 2004 0.004*** 0 0.000** 0.000** [2.59] [0.13] [2.26] [2.23]Number of Observations 361 729 1,406 2,488

Thus it seems that if the electorates do not experience growth they elect

candidates based on other factors such as caste and other social or economic allegiances

but if they experience growth, they base their vote more on economic performance and

less on other factors. The recent election in Bihar also lends some support to this set of

conclusions.

49

8. Concluding Remarks

This paper provides the first analysis of the relationship of growth to election

outcomes in India. It also provides the first comprehensive quantitative analysis of the

2009 Lok Sabha elections. We assembled a comprehensive data set consisting of all

candidates contesting the election. Our key results are robust to virtually every

modification that seemed intuitively plausible to us.

Our main results may be summarized as follows. First, personal characteristics

such as education and wealth have at most a small impact on election outcomes even

though this is not apparent from the unconditional within group probabilities. Second, at

least in the 2009 election, incumbency at all levels—candidate, national ruling party and

state ruling party—contributed positively to election prospects of a candidate. Here we

use “incumbency” in the ex post sense and it does not control for differences that may

arise from unobservable characteristics. Finally, our key result is that superior growth

performance at the level of the state gives a definite advantage to the candidates of the

incumbent party in the constituencies of that state. State elections in Bihar, which took

place after the first draft of this paper had been presented at a conference at Columbia

University, reinforces this finding. Chief Minister Nitish Kumar, who placed Bihar on a

high growth trajectory after decades of slow growth won a landslide victory over his

rivals who presented themselves as the representatives of the socially disadvantaged but

have had a poor record of economic performance in the past.

Two final points may be noted. Growth itself may be correlated with several

attributes that the voters value. For example, superior growth performance may be

positively correlated with good governance including law and order. It may also be

50

associated with reduced levels of poverty. Therefore, there remains scope for differences

of opinion on whether the voters rewarded growth in the 2009 elections or other variables

with which it might be correlated. From the policy perspective, we do not see this as a

major issue, however. Even if it is these other variables that voters value over growth per

se, growth would serve as a reasonable target variable for the state politicians to win the

elections.

Second, none of what has been said above rules out the possibility that specific

important events such as mega-corruption scandals, a sympathy wave resulting, for

example, from the assassination of a particularly popular leader of a party or a national

emergency will not sway the outcome in a future election. What does seem plausible is

that, when alternative defining issues are absent, growth is likely to be increasingly

central to determining election outcomes. 

51

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Appendix A: Incumbency at the Candidate Level We matched the names of the MPs in the candidates’ list for 2009. In order to make sure that we have not omitted any names of the incumbents we carried out a detailed search on each MPs name which we could not track down in the candidate database for 2009 elections. Here is a summary of these data:

MPs in 2004 Parliament and Election Candidates in 2009

Total Nominated Anglo Indian members

545 2

Contested 384 Won 2009 179 Lost 2009 205 Did not contest 159 Reasons for not contesting in 2009 Health, Age, Death 10

Candidate contested an Assembly Election 22

Ticket not given 64 Change of Party 16 Corruption Charges 8 Delimitation* 8 Other** 31

*The 2009 elections adopted re-drawn electoral constituencies based on the census, following the 2002 Delimitation Commission of India, whose recommendations were approved in February 2008. Based on this exercise 499 out of the total 543 Parliamentary constituencies were newly delimited constituencies. This affected the National Capital Region of Delhi, the Union Territory of Puducherry and all the states except Arunachal Pradesh, Assam, Jammu & Kashmir, Jharkhand, Manipur and Nagaland.

**These are the sitting MPs whom we could not track in the candidates list for 2009 elections and could not find additional information to determine why they did not contest the elections.

Thus, 30 percent of the MPs did not contest the election in 2009, out of which 14 percent did not contest because they contested, or wanted to contest, an assembly election, 10 percent of the candidates changed parties and decided not to contest or were not fielded as a candidate by their new party; 5 percent of the candidates did not seek reelection because their constituencies were redrawn completely due to delimitation and perhaps the reservation category changed for some of them, or it was difficult for their party to decide which of the candidates to nominate from which constituencies; 6 percent of the MPs did not seek reelection due to health or age related reasons. From the 157 MPs whom we could track, we could not find the reasons to not contest election in 31 cases. Finally, another 40 percent of the candidates were denied tickets for reasons unknown to us, and could very well be due to one of the reasons cited above.

54

Appendix B: Incumbent Party(ies) in the States

State State Code Year of Incumbency Main Incumbent Party Main Coalition Andhra Pradesh AP 2004 INC UPA Bihar BH 2005 JDU, BJP NDA Chhattisgarh CT 2003 BJP NDA Goa GA 2007 INC UPA Gujarat GJ 2007 BJP NDA Haryana HY 2005 INC UPA Himachal Pradesh HP 2007 BJP NDA Jharkhand JD 2005 BJP NDA Kerala KL 2006 CPIM III Front Madhya Pradesh MP 2003 BJP NDA Maharashtra MH 2004 INC, NCP UPA Nct Of Delhi DL 2003 INC UPA Orissa OR 2004 BJD III Front Punjab PJ 2007 SAD NDA Rajasthan RJ 2003 BJP NDA Tamil Nadu TN 2006 DMK UPA Uttar Pradesh UP 2007 BSP III Front Uttarakhand UL 2007 BJP NDA West Bengal WB 2006 CPIM III Front